{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 用子弹图进行对比分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. 初始设置\n",
    "\n",
    "首先，导入所需的库，并设置中文字体等。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入所需的库\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as image\n",
    "\n",
    "# 正常显示中文标签\n",
    "mpl.rcParams['font.sans-serif'] = ['SimHei']\n",
    "\n",
    "# 自动适应布局\n",
    "mpl.rcParams.update({'figure.autolayout': True})\n",
    "\n",
    "# 正常显示负号\n",
    "mpl.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. 开始画图\n",
    "\n",
    "用「**面向对象**」的方法画图。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      ],
      "text/plain": [
       "<Figure size 576x187.2 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
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     "output_type": "display_data"
    }
   ],
   "source": [
    "# 使用「面向对象」的方法画图\n",
    "fig, ax = plt.subplots(figsize=(8, 2.6))\n",
    "\n",
    "# 设置标题和坐标轴标签\n",
    "ax.set_title(\"2020年3月，销售收入超额完成目标\\n\", fontsize=26, verticalalignment='bottom')\n",
    "\n",
    "# 定义指标名称\n",
    "y = '销售收入'\n",
    "\n",
    "# 背景放在最底层\n",
    "ax.barh(y, 200, height=0.8, color='#00589F', alpha=0.40, label='差')\n",
    "ax.barh(y, 50, height=0.8, left=200, color='#00589F', alpha=0.25, label='中')\n",
    "ax.barh(y, 50, height=0.8, left=250, color='#00589F', alpha=0.1, label='好')\n",
    "\n",
    "# 隐藏的占位元素，为了让图片留有更多的空白区域\n",
    "ax.barh(y, 50, height=1, left=250, color='#00589F', alpha=0)\n",
    "\n",
    "# 实际值\n",
    "ax.barh(y, 280, height=0.3, color='#00589F', label='实际')\n",
    "\n",
    "# 目标值\n",
    "lc = ax.vlines(260, -0.3, 0.3, color='#CC5036', label='目标')\n",
    "# 设置线条宽度\n",
    "lc.set_linewidth(3)\n",
    "\n",
    "# 隐藏边框\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "\n",
    "# X 轴的刻度线朝内，调整线条的长度，让其贴近条形图\n",
    "mpl.rcParams['xtick.direction'] = 'in'\n",
    "ax.tick_params(axis='x', which='major', length=8)\n",
    "\n",
    "# 隐藏 Y 轴刻度\n",
    "ax.set_yticks([])\n",
    "\n",
    "# 设置 X 轴标签\n",
    "ax.set_xlabel('\\n（单位：万元）', fontsize=16)\n",
    "\n",
    "# 设置坐标标签字体大小\n",
    "ax.tick_params(labelsize=20)\n",
    "\n",
    "# 设置图例显示的位置\n",
    "ax.legend(bbox_to_anchor=(1.15, 1.15))\n",
    "\n",
    "# 在另一个坐标轴中添加 LOGO 图片\n",
    "im = image.imread('../linji.jpg')\n",
    "ax2 = fig.add_axes([0.75, -0.15, 0.08, 0.26])\n",
    "ax2.imshow(im, aspect='auto', extent=(0, 1, 0, 1), zorder=-1)\n",
    "# 隐藏刻度线\n",
    "ax2.set_xticks([])\n",
    "ax2.set_yticks([])\n",
    "# 隐藏边框\n",
    "ax2.spines['top'].set_visible(False)\n",
    "ax2.spines['right'].set_visible(False)\n",
    "ax2.spines['bottom'].set_visible(False)\n",
    "ax2.spines['left'].set_visible(False)\n",
    "\n",
    "# 设置字体\n",
    "font={'family':'SimHei', 'color':'#00589F', 'size':15}\n",
    "\n",
    "# 标示制图的作者信息\n",
    "ax2.text(1, 0.2, ' 制图：林骥\\n' + r'$@$' + '数据化分析', fontdict=font)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 关于作者：林骥，从 2008 年开始从事数据分析工作，坚持用心做原创，用数据化解难题，让分析更加有效，@数据化分析。\n",
    "\n",
    "![ ](https://tva1.sinaimg.cn/large/00831rSTgy1gdphsox783j30pv0b0q4q.jpg)\n",
    "\n",
    "▲ 扫码进入公众号后台，发送「入群」，加入林骥的读者交流群。\n",
    "\n",
    "转载请保留作者信息，并注明来源：https://github.com/linjiwx/mp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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